For the temporal action detection task, the proposed BCNet also achieves superior results, as shown in Table REF .
The performance of our method exceeds state-of-the-art proposal generation methods by a big margin at different tIoU thresholds.
Specially, BCNet based on TSN feature reaches an mAP of 51.6 \(\%\)  at IoU 0.5.
Besides, the performance of BCNet can be further boosted when
it is combined with proposal post-processing methods: P-GCN [1]} and MUSES [2]}.
Now BCNet reaches 60.0% at IoU 0.5, outperforming all the other methods.
This signifies the advantage of BCNet proposals regardless of post-processing.
